Artificial intelligence (AI) is one of the most important technological advancements.
Understanding all of the facets of AI can be confusing for the newly initiated. A good place to start is by understanding the difference between the concepts of machine learning and deep learning.
One of the first things you should understand is that deep learning is actually a type of machine learning. Deep learning utilizes a programmable neural network to provide machines with the ability to make decisions without input from a human. This is considered to be an evolution of the concept of machine learning.
On a basic level, machine learning is a set of algorithms that parse data, learn from it and then apply that knowledge to decision making. One example of machine learning is how your favorite streaming service recommends to you other media it has determined you would enjoy based on your past media consumption.
Machine learning applications utilize a variety of development techniques, including CICD, and can perform many different kinds of automated tasks. AI algorithms are constantly learning, which helps them to simulate human interaction. The defining aspect of machine learning is that it allows the software to get better at a task the more it learns about doing that task.
While deep learning is a more advanced type of machine learning, there are some differences. Machine learning applications can get better at whatever they have been programmed to do over time. However, machine learning programs still require some guidance from humans. For example, if your Netflix program recommends a movie to you that you do not like, it needs your input to help it learn that it made a mistake. This is why most of these types of services have some sort of system for users to rate content. A deep learning program is capable of figuring out whether or not it made a mistake and what that mistake was on its own.
Deep learning software works by repeatedly analyzing data to reach conclusions, similar to how humans would. To do this, the software makes use of a layered structure of algorithms known as an artificial neural network. Artificial neural networks are patterned on the human brain and are capable of learning in ways that standard machine learning tools are not.
Many modern customer service applications utilize machine learning algorithms. This is the technology behind most self-service applications. It is also used to increase the productivity of human customer service workers and improve workflows.
The data that powers these applications comes from customer queries. These applications can cross-reference the queries with the context in which they are made to predict what customers will want and need.
Customer service is not the only field being revolutionized by deep learning applications. Much of the technology behind self-driving cars depends upon deep learning applications. These cars use sensors to take in information, similarly to how humans use their eyes, ears and other senses. As the cars encounter more driving situations they learn and get better at driving, much like a human driver does.
Other examples of deep learning include using AI to better detect and treat illnesses, voice-activated assistants, automatic text generation, automatic handwriting generation, image recognition, advertising, earthquake predictions and market forecasting. Most of these technologies are still being developed and refined, but it is reasonable to expect that many will advance to the point where they become a common part of daily life.
Machine learning and deep learning are two of the core concepts behind artificial intelligence. Machine learning is already in use in many of the products and services people enjoy today. Deep learning goes a step beyond machine learning and is expected to play a major role in the technology of the near future.